TY - JOUR
T1 - Anomaly Detection in Graph Signals With Complex Wavelet Packet Correlation Mining
AU - Sun, Xuandi
AU - Nassif, Roula
AU - Richard, Cédric
AU - Yang, Ziye
AU - Chen, Jie
AU - Wang, Haiyan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Data generated by network-structured applications, such as sensor networks or communication networks, typically reside on complex and irregular structures. These data necessitate specific graph signal processing tools to harness their characteristics. Detecting anomalous events in graph signals is significant in enhancing reliability of systems, where anomalies often activate localized groups of vertices. In this paper, we introduce a novel approach, the Joint Graph Wavelet Canonical Correlation Analysis, for detecting anomalies in graph signals through cooperative filtering while identifying their locations. This approach conducts canonical correlation analysis on graph signals to achieve data fusion within the wavelet domain while accounting for the graph topology. Subsequently, we devise an optimization algorithm specifically tailored for anomaly detection in graph signals. Finally, we illustrate its effectiveness through numerical simulations on synthetic data and by presenting test results from a multi-microphone network.
AB - Data generated by network-structured applications, such as sensor networks or communication networks, typically reside on complex and irregular structures. These data necessitate specific graph signal processing tools to harness their characteristics. Detecting anomalous events in graph signals is significant in enhancing reliability of systems, where anomalies often activate localized groups of vertices. In this paper, we introduce a novel approach, the Joint Graph Wavelet Canonical Correlation Analysis, for detecting anomalies in graph signals through cooperative filtering while identifying their locations. This approach conducts canonical correlation analysis on graph signals to achieve data fusion within the wavelet domain while accounting for the graph topology. Subsequently, we devise an optimization algorithm specifically tailored for anomaly detection in graph signals. Finally, we illustrate its effectiveness through numerical simulations on synthetic data and by presenting test results from a multi-microphone network.
KW - Anomaly detection
KW - canonical correlation analysis
KW - cooperative filtering
KW - dual-tree complex wavelet packet transform
KW - graph Laplacian regularization
KW - sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85210283145&partnerID=8YFLogxK
U2 - 10.1109/TCSI.2024.3491152
DO - 10.1109/TCSI.2024.3491152
M3 - 文章
AN - SCOPUS:85210283145
SN - 1549-8328
JO - IEEE Transactions on Circuits and Systems I: Regular Papers
JF - IEEE Transactions on Circuits and Systems I: Regular Papers
ER -